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- W2891633827 abstract "We develop and implement an efficient algorithm to estimate the 5parameters of Heston's model from arbitrary given series of jointobservations for the stock price and volatility. We consider the timeinterval T separating two observations to be unknown andestimate it from the data, thereby estimating 6 parameterswith a clear gain in fit accuracy. We compare the maximum likelihoodparameter estimates based on an Euler discretization scheme to analogous estimatesderived from the more accurate Milstein discretization scheme; we derive explicit conditionsunder which the two set of estimates are asymptotically equivalent, and we compute the asymptotic distribution ofthe difference of the two set of estimates. We show that parameter estimates derived from the Euler scheme byconstrained optimization of the approximate maximum likelihood are consistent, and we compute their asymptotic variances. Numerically, our estimation algorithms are easy to implement,and require only very moderate amounts of CPU. We have performed extensive simulations which show that for standard range of the process parameters, the empirical variances of our parameter estimates are correctly approximated by their theoretical asymptotic variances." @default.
- W2891633827 created "2018-09-27" @default.
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- W2891633827 date "2009-01-01" @default.
- W2891633827 modified "2023-09-26" @default.
- W2891633827 title "Accurate parameter estimation for coupled stochastic dynamics" @default.
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- W2891633827 doi "https://doi.org/10.3934/proc.2009.2009.44" @default.
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